A preference-based multi-objective evolutionary algorithm R-NSGA-II with stochastic local search

Filatovas, Ernestas; Lančinskas, Algirdas; Kurasova, Olga; Žilinskas, Julius

doi:10.1007/s10100-016-0443-x

Cited by 23 publications

(9 citation statements)

References 31 publications

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“…The selected rectangles are the balanced options between local and global search considering the central value and size of the corresponding regions. Notice the similitude of this approach to computing the Pareto set as the solution to a multi-objective optimization problem (Filatovas et al, 2017 ).…”

Section: Methodsmentioning

confidence: 99%

Black-box and surrogate optimization for tuning spiking neural models of striatum plasticity

Cruz

González-Redondo²,

Redondo³

et al. 2022

Front. Neuroinform.

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The basal ganglia (BG) is a brain structure that has long been proposed to play an essential role in action selection, and theoretical models of spiking neurons have tried to explain how the BG solves this problem. A recently proposed functional and biologically inspired network model of the striatum (an important nucleus of the BG) is based on spike-timing-dependent eligibility (STDE) and captured important experimental features of this nucleus. The model can recognize complex input patterns and consistently choose rewarded actions to respond to such sensory inputs. However, model tuning is challenging due to two main reasons. The first is the expert knowledge required, resulting in tedious and potentially biased trial-and-error procedures. The second is the computational cost of assessing model configurations (approximately 1.78 h per evaluation). This study addresses the model tuning problem through numerical optimization. Considering the cost of assessing solutions, the selected methods stand out due to their low requirements for solution evaluations and compatibility with high-performance computing. They are the SurrogateOpt solver of Matlab and the RBFOpt library, both based on radial basis function approximations, and DIRECT-GL, an enhanced version of the widespread black-box optimizer DIRECT. Besides, a parallel random search serves as a baseline reference of the outcome of opting for sophisticated methods. SurrogateOpt turns out to be the best option for tuning this kind of model. It outperforms, on average, the quality of the configuration found by an expert and works significantly faster and autonomously. RBFOpt and the random search share the second position, but their average results are below the option found by hand. Finally, DIRECT-GL follows this line becoming the worst-performing method.

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Section: Methodsmentioning

confidence: 99%

Black-box and surrogate optimization for tuning spiking neural models of striatum plasticity

Cruz

González-Redondo²,

Redondo³

et al. 2022

Front. Neuroinform.

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“…In [22] an interactive algorithm based on R-NSGA-II is proposed, and in [23] R-NSGA-II is modified by integrating a stochastic local search in a memetic fashion, see [47], [10], [32], and [72].…”

Section: Progressive Preference Articulation: a Brief Reviewmentioning

confidence: 99%

Progressive preference articulation for decision making in multi-objective optimisation problems

Rostami

Neri

Epitropakis

2017

ICA

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Abstract.This paper proposes a novel algorithm for addressing multi-objective optimisation problems, by employing a progressive preference articulation approach to decision making. This enables the interactive incorporation of problem knowledge and decision maker preferences during the optimisation process. A novel progressive preference articulation mechanism, derived from a statistical technique, is herein proposed and implemented within a multi-objective framework based on evolution strategy search and hypervolume indicator selection. The proposed algorithm is named the Weighted Z-score Covariance Matrix Adaptation Pareto Archived Evolution Strategy with Hypervolume-sorted Adaptive Grid Algorithm (WZ-HAGA).WZ-HAGA is based on a framework that makes use of evolution strategy logic with covariance matrix adaptation to perturb the solutions, and a hypervolume indicator driven algorithm to select successful solutions for the subsequent generation. In order to guide the search towards interesting regions, a preference articulation procedure composed of four phases and based on the weighted z-score approach is employed. The latter procedure cascades into the hypervolume driven algorithm to perform the selection of the solutions at each generation.Numerical results against five modern algorithms representing the state-of-the-art in multi-objective optimisation demonstrate that the proposed WZ-HAGA outperforms its competitors in terms of both the hypervolume indicator and pertinence to the regions of interest.

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“…For these reasons, some MO memetic approaches have been proposed in the literature. However, we can find approaches that are mostly application-specific [53] or that employ heuristic [33,34,36,41], meta-heuristic [1,52], stochastic [15,19] or scalarization-based [31,48,51] local search steps. Even the few proposed strategies employing gradient information for the local search steps do not exploit the concept of common descent directions.…”

Section: Introductionmentioning

confidence: 99%

A Memetic Procedure for Global Multi-Objective Optimization

Lapucci¹,

Mansueto²,

Schoen³

2022

Preprint

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In this paper we consider multi-objective optimization problems over a box. The problem is very relevant and several computational approaches have been proposed in the literature. They broadly fall into two main classes: evolutionary methods, which are usually very good at exploring the feasible region and retrieving good solutions even in the nonconvex case, and descent methods, which excel in efficiently approximating good quality solutions. In this paper, first we confirm, through numerical experiments, the advantages and disadvantages of these approaches. Then we propose a new method which combines the good features of both. The resulting algorithm, which we call Non-dominated Sorting Memetic Algorithm (NSMA), besides enjoying interesting theoretical properties, excels in all of the numerical tests we performed on several, widely employed, test functions.

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A preference-based multi-objective evolutionary algorithm R-NSGA-II with stochastic local search

Cited by 23 publications

References 31 publications

Black-box and surrogate optimization for tuning spiking neural models of striatum plasticity

Black-box and surrogate optimization for tuning spiking neural models of striatum plasticity

Progressive preference articulation for decision making in multi-objective optimisation problems

A Memetic Procedure for Global Multi-Objective Optimization

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